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How to Prepare for a Senior Data Engineer Interview in 30 Days

dT
dTechStack Team
Apr 21, 2026
8 min read·Career
CareerInterviewData EngineeringStudy Plan

A 30-day study plan: Spark internals (week 1), Kafka + streaming (week 2), system design (week 3), SQL + case study (week 4).

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Senior data engineer interviews at companies like Flipkart, Swiggy, Zepto, and CRED are technically demanding. They're not testing whether you can write a groupBy — they're testing whether you understand why things work the way they do.

What They Actually Test

Spark internals (not just API). How does AQE work? What happens during a shuffle? How do you fix data skew?

Kafka + streaming design. Consumer group semantics, exactly-once, consumer lag.

SQL and data modeling. Window functions, SCD Type 2, recursive CTEs.

System design. Lambda vs Kappa, data mesh, designing a data platform for 100TB/day.

Week 1: Spark Internals

Day 1–2: Read Spark's execution model — stages, tasks, shuffle.

Day 3–4: Enable AQE locally, run a query with/without, read EXPLAIN output.

Day 5–7: Practice explaining data skew. What are the five ways to fix it?

scala
// Week 1 practice: explain what happens
val df = spark.read.parquet("s3://events/")
  .filter($"event_type" === "purchase")
  .join(users, "user_id")
  .groupBy($"country")
  .agg(sum($"amount").as("revenue"))
// How many stages? Where is shuffle? If users is 50MB, what optimization?
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Week 2: Kafka + Streaming

Day 8–10: Consumer groups — draw partition assignment for 6 partitions, 3 consumers.

Day 11–12: Exactly-once: idempotent producers, transactions, and where EOS breaks.

Day 13–14: System design: "Design a real-time pipeline at 100K events/sec."

Week 3: System Design

Day 15–17: Lambda vs Kappa — build an argument for each.

Day 18–19: Lakehouse: Delta vs Iceberg.

Day 20–21: Data mesh — what does a self-serve platform look like?

Use STAR format: Situation, Task, Action, Result.

Week 4: SQL + Case Study

Day 22–23: Window functions from memory: RANK, ROW_NUMBER, LAG.

Day 24–25: SCD Type 2 in dbt with snapshots AND in pure SQL with MERGE.

Day 26–28: Case study: "Revenue report shows ₹50L less than finance — debug it."

Day 29–30: Mock interviews. Record yourself. The way you explain matters as much as the answer.

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dT
dTechStack Team
Data Engineering · dTechStack Ventures · Bengaluru